RETRACTED: Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine

Lu, Siyuan; Liu, Shuaiqi; Wang, Shuihua; Zhang, Yudong

doi:10.3389/fncom.2021.738885

Cited by 8 publications

(8 citation statements)

References 39 publications

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“…In this study, five out of 9 patients with severe HACE who underwent SWI scans showed CMBs, which were significant in the corpus callosum, and one patient with diffuse CMBs died. Some studies suggest that CMBs can lead to cognitive dysfunction and increase the risk of stroke ( 28 , 33 ) and even death ( 37 ). Therefore, we believe that patients developing CMBs often have poor prognoses and should be treated promptly.…”

Section: Discussionmentioning

confidence: 99%

Study of high-altitude cerebral edema using multimodal imaging

Long

Bao²

2023

Front. Neurol.

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ObjectiveTo analyze the brain imaging features of high-altitude cerebral edema (HACE) using computed tomography (CT) and multi-sequence magnetic resonance imaging (MRI) and to explore its injury characteristics.Materials and methodsWe selected 30 patients with HACE diagnosed between January 2012 to August 2022 as the experimental group and 60 patients with dizziness on traveling from the plain to the plateau or from lower altitude to higher altitude in a short period of time as the control group. We collected general clinical data from the experimental group and classified it according to clinical symptoms. In both groups, we then performed a head CT and multi-sequence MRI (T1WI, T2WI, FLAIR, and DWI). Among them, nine patients with HACE were also scanned using susceptibility-weighted imaging (SWI). Finally, we analyzed the images.ResultsAccording to clinical symptoms, we divided the 30 cases of HACE into 12 mild cases and 18 severe cases. There was no significant difference in sex, age, leukocyte, neutrophil, or glucose content between mild and severe HACE. The sensitivity and specificity of the MRI diagnosis were 100 and 100%, respectively, while the sensitivity and specificity of the CT diagnosis were 23.3 and 100%, respectively. The distribution range of deep and juxtacortical white matter edema was significantly larger in severe HACE than in mild HACE (p < 0.001). The corpus callosum edema distribution range in severe HACE was significantly larger than that in mild HACE (p = 0.001). The ADC value of the splenium of the corpus callosum was significantly lower in severe HACE than in mild HACE (p = 0.049). In mild and severe HACE, the signal intensity of the DWI sequence was significantly higher than that of conventional MRI sequences (T1WI, T2WI, FLAIR) (p = 0.008, p = 0.025, respectively). In severe HACE, seven cases showed bilateral corticospinal tract edema at the thalamic level, and SWI showed cerebral microbleeds (CMBs) in five cases, especially in the corpus callosum.ConclusionsMRI has more advantages than CT in the evaluation of HACE, especially in the DWI sequence. The white matter injury of severe HACE is more severe and extensive, especially in the corpus callosum, and some CMBs and corticospinal tract edema may also appear.

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Section: Discussionmentioning

confidence: 99%

Study of high-altitude cerebral edema using multimodal imaging

Long

Bao²

2023

Front. Neurol.

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“…Finally, there are convolutional neural networks (CNN) [168], which are the most popular solution [22,53,78,77,23,124,75,49,72].…”

Section: Neural Network-based Methodsmentioning

confidence: 99%

“…It might be performed using the sliding neighborhood processing (SNP) technique to produce smaller fragments of the original image. A lot of works utilized this method: [46,22,53,48,47,52,139,4,14,77,23,124,125,75,49].…”

Section: Pre-processingmentioning

confidence: 99%

Review of methods for automatic cerebral microbleeds detection

Ferlin¹,

Klawikowska²,

Grzywińska³

et al. 2023

Preprint

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Cerebral microbleeds detection is an important and challenging task. With the gaining popularity of the MRI, the ability to detect cerebral microbleeds also raises. Unfortunately, for radiologists, it is a time-consuming and laborious procedure. For this reason, various solutions to automate this process have been proposed for several years, but none of them is currently used in medical practice. In this context, the need to systematize the existing knowledge and best practices has been recognized as a factor facilitating the imminent synthesis of a real CMBs detection system practically applicable in medicine. To the best of our knowledge, all available publications regarding automatic cerebral microbleeds detection have been gathered, described, and assessed in this paper in order to distinguish the current research state and provide a starting point for future studies.

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“…It consists of input layer, hidden layer and output layer [30]. Each layer consists of several neurons, each neuron receives the output of the previous layer as input, and then generates output to the next layer [31], which belongs to one-way propagation [32]. According to the number of hidden layers, it is divided into single-layer feedforward neural network and multi-layer feed-forward neural network.…”

Section: Extreme Learning Machinementioning

confidence: 99%

COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine

Han

Wang

2022

EAI Endorsed Trans e-Learn

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In recent years, COVID-19 has spread rapidly among humans. Chest CT is an effective means of diagnosing COVID-19. However, the diagnosis of CT images still depends on the doctor's visual judgment and medical experience. This takes a certain amount of time and may lead to misjudgment. In this paper, a new algorithm for automatic diagnosis of COVID-19 based on chest CT image data was proposed. The algorithm comprehensively uses WE to extract image features, uses ELM for training, and finally passes k-fold CV validation. After evaluating and detecting performance on 296 chest CT images, our proposed method is superior to state-of-the-art approaches in terms of sensitivity, specificity, precision, accuracy, F1, MCC and FMI.

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RETRACTED: Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine

Cited by 8 publications

References 39 publications

Study of high-altitude cerebral edema using multimodal imaging

Study of high-altitude cerebral edema using multimodal imaging

Review of methods for automatic cerebral microbleeds detection

COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine

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